The sublimation materials Unit

Sublimation materials lab photo.

The sublimation materials unit focuses on exploring silicon carbide semiconductors, graphene, other low-dimensional materials, and their energy and environmental applications.

Sublimation materials unit focuses on exploring silicon carbide semiconductors, graphene, other low-dimensional materials, and their energy and environmental applications.

Sublimation materials Unit lab photo.The group is internationally recognized with its expertise in sublimation growth of silicon carbide (SiC) and graphene growth on SiC substrates, particularly the cubic silicon carbide (3C-SiC) growth of bulk like material with high structural quality.

The unique sublimation growth equipment in Sweden is a strong prerequisite of further technological developments. In 2011 the spin off company Graphensic AB for production of high-quality, wafer-scale graphene on SiC substrates was launched.

The group also focuses on exploring graphene, 3C-SiC, and other materials for a direct conversion of solar energy into the renewable energy such as hydrogen and other chemical fuels based on photocatalytic and photoelectrochemical (PEC) water splitting and PEC CO2 reduction. It also aims to tailor material properties for novel sensing, catalysis, and optoelectronic applications.

Publications

2026

Ivan I. Shtepliuk, Lingyin Meng, Christer Borgfeldt, Jens Eriksson, Donatella Puglisi (2026) Biomarker‐Agnostic Detection of Ovarian Cancer from Blood Plasma Using a Machine Learning‐Driven Electronic Nose Advanced Intelligent Systems, Article e202500838 (Article in journal) Continue to DOI

2025

Donatella Puglisi, Jens Eriksson, Kerstin Montelius, Emanuela Tavaglione, Barbara Fabbri, Rebecca Stenberg, Ivan I. Shtepliuk (2025) Machine Learning-Enhanced Odor Detection System asNext-Generation Forensic Technology AMA Proceedings, p. 120-121 (Conference paper) Continue to DOI
Donatella Puglisi, Ivan I. Shtepliuk, Jens Eriksson (2025) Integration of Machine Learning for Next Generation Gas Sensor Technology Sensors and Microsystems: Proceedings of AISEM 2025, p. 399-403 (Conference paper) Continue to DOI
Jens Eriksson, Donatella Puglisi, Filip Herbst, Arturas Dobilas, Ivan Shtepliuk, Ulrika Joneborg, Henrik Falconer, Angelique Floter Radestad, Christer Borgfeldt (2025) Machine learning-enhanced gas sensor technology identifies ovarian and endometrial cancer of all stages through plasma volatile organic compound patterns eBioMedicine, Vol. 122, Article 106027 (Article in journal) Continue to DOI
Jens Eriksson, Ivan Shtepliuk, Donatella Puglisi (2025) Toward self-learning sensor devices for precision molecular identification Chem, Vol. 11, Article 102612 (Article in journal) Continue to DOI
Ivan Shtepliuk, Kerstin Montelius, Jens Eriksson, Donatella Puglisi (2025) Adaptive Machine Learning for Electronic Nose-Based Forensic VOC Classification Advanced Science, Vol. 12, Article e04657 (Article in journal) Continue to DOI
Jui-Che Chang, Justinas Palisaitis, Shailesh Kalal, Gueorgui Kostov Gueorguiev, Axel Persson, Eric Nestor Tseng, Grzegorz Greczynski, Per O A Persson, Jianwu Sun, Yu-Kuei Hsu, Lars Hultman, Jens Birch, Ching-Lien Hsiao (2025) The Role of a Ta2O5 Seed Layer on Phase Evolution and Epitaxial Growth of Ta3N5 Thin Films on Al2O3(0001) ACS Applied Energy Materials, Vol. 8, p. 6699-6706 (Article in journal) Continue to DOI
Hui Zeng, Satoru Yoshioka, Weimin Wang, Zhongyuan Han, Ivan Gueorguiev Ivanov, Hongwei Liang, Vanya Darakchieva, Jianwu Sun (2025) Manipulating Electron Structure through Dual-Interface Engineering of 3C-SiC Photoanode for Enhanced Solar Water Splitting Journal of the American Chemical Society, Vol. 147, p. 14815-14823 (Article in journal) Continue to DOI
Ivan I. Shtepliuk, Guillem Domènech-Gil, Viktor Almqvist, Arja Helena Kautto, Ivar Vågsholm, Sofia Boqvist, Jens Eriksson, Donatella Puglisi (2025) Electronic nose and machine learning for modern meat inspection Journal of Big Data, Vol. 12, Article 96 (Article in journal) Continue to DOI
Ivan Shtepliuk, Donatella Puglisi, Jens Eriksson (2025) Computational insights into the adsorption of lung cancer biomarkers on graphene-based materials and interfacial phenomena Applied Surface Science, Vol. 696, Article 162985 (Article in journal) Continue to DOI

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